Speaker
Dimitrios Athanasakos
Description
We introduce a complete basis of subjets for machine learning-based jet tagging. The subjets are obtained with (i) a fixed radius or (ii) the clustering is performed until a fixed number of subjets is obtained.
For nonzero values of the subjet radius, the resulting classifier is Infrared-Collinear (IRC) safe. By lowering the subjet radius, we can increase the sensitivity to nonperturbative physics. In the limit of a vanishing subjet radius, the exclusive subjet basis approximates deep sets/particle flow networks (IRC unsafe). The basis introduced here is thus ideally suited to quantify the information content of jets at the boundary of perturbative vs. nonperturbative physics.
Primary authors
Dimitrios Athanasakos
Felix Ringer
(Stony Brook University)
James Mulligan
(University of California, Berkeley (US))
Andrew Larkoski
(SLAC National Accelerator Laboratory)
Mateusz Ploskon
(Lawrence Berkeley National Lab. (US))
